Datum Daily
Issue #103·Tuesday, April 14, 2026

Data insights for every level.

Datum Daily covers the latest in data analytics, data science, data engineering, and applied statistics — from foundational concepts to cutting-edge research.

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Why Your Dashboards Are Lying to You (And How to Fix It)
Data Analytics

Why Your Dashboards Are Lying to You (And How to Fix It)

Most dashboards are not built to deceive. They are built in a hurry, by well-meaning analysts who never stopped to ask: what story does this chart actually tell?

Apr 2, 2026·9 min read
Data Science

Overfitting, Explained Simply (With a Real-World Analogy)

Mar 28, 2026
Overfitting, Explained Simply (With a Real-World Analogy)
Data Engineering

Building Data Pipelines That Don't Break at 3 AM

Mar 25, 2026
Building Data Pipelines That Don't Break at 3 AM
Applied Statistics

p-Values: What They Actually Mean (And What They Don't)

Mar 20, 2026
p-Values: What They Actually Mean (And What They Don't)
Latest
Overfitting, Explained Simply (With a Real-World Analogy)
Data Science

Overfitting, Explained Simply (With a Real-World Analogy)

Overfitting is when your model learns the training data so well that it memorizes the noise along with the signal. It is the machine learning equivalent of cramming for an exam.

Mar 28, 2026·7 min read
Building Data Pipelines That Don't Break at 3 AM
Data Engineering

Building Data Pipelines That Don't Break at 3 AM

The dirty secret of data engineering is that most pipelines are fragile. They work fine in development, pass QA, and then fail silently in production at the worst possible moment.

Mar 25, 2026·15 min read
p-Values: What They Actually Mean (And What They Don't)
Applied Statistics

p-Values: What They Actually Mean (And What They Don't)

A p-value of 0.04 does not mean there is a 96% chance your hypothesis is correct. It does not even mean your result is important. Here is what it actually means.

Mar 20, 2026·8 min read
Predicting Customer Churn with Survival Analysis
Data Analytics

Predicting Customer Churn with Survival Analysis

Most churn models treat the problem as binary: churned or not churned. Survival analysis asks a better question: when will a customer churn, and what factors accelerate or delay it?

Mar 15, 2026·13 min read
Breaking Into Data Science in 2026: An Honest Roadmap
Career & Education

Breaking Into Data Science in 2026: An Honest Roadmap

The data science job market has changed dramatically. Here is what a realistic, modern path into the field actually looks like in 2026.

Mar 15, 2026·10 min read
Feature Engineering: The Skill That Separates Good Models from Great Ones
Data Science

Feature Engineering: The Skill That Separates Good Models from Great Ones

In most real-world machine learning projects, the biggest performance gains do not come from switching algorithms. They come from better features.

Mar 10, 2026·14 min read
dbt for Analytics Engineers: A Practical Introduction
Data Engineering

dbt for Analytics Engineers: A Practical Introduction

dbt brings software engineering best practices to the analytics workflow: version control, testing, documentation, and modular SQL that actually scales.

Mar 5, 2026·13 min read
The Four Assumptions of Linear Regression (And What Happens When You Violate Them)
Applied Statistics

The Four Assumptions of Linear Regression (And What Happens When You Violate Them)

Most practitioners fit a linear regression, check the R-squared, and move on. But R-squared tells you nothing about whether the model's assumptions are met — and violated assumptions mean your coefficients, standard errors, and p-values are wrong.

Feb 28, 2026·18 min read
MS in Data Science vs. MS in Statistics: Which Degree is Right for You?
Career & Education

MS in Data Science vs. MS in Statistics: Which Degree is Right for You?

Both degrees will make you more rigorous. But they are not the same degree, and the differences matter more than most program websites will tell you.

Feb 10, 2026·8 min read
Data Snippets
Quick insights · Under 2 min read
Pro TipApr 3

Always check your join cardinality before aggregating

Before you run a GROUP BY after a JOIN, verify the cardinality of the join. A many-to-many join silently inflates your row count, causing double-counting in any SUM or COUNT that follows. Run SELECT COUNT(*) and SELECT COUNT(DISTINCT key) on both sides of the join first. If they differ, you have a fan-out problem — and your aggregations are wrong.

#SQL#DataEngineering#BestPractices
By the NumbersApr 2

The global data engineering job market grew 50% in 2025

According to LinkedIn's 2025 Jobs on the Rise report, Data Engineer was the fastest-growing technical role for the third consecutive year, with a 50% year-over-year increase in job postings. The median base salary in the US reached $148,000. Cloud data platforms (Snowflake, Databricks, BigQuery) and dbt proficiency were the most-cited skills in job descriptions.

Source: LinkedIn Jobs on the Rise 2025

#Career#DataEngineering#IndustryTrends
DefinitionApr 1

What is a Confidence Interval — in plain English

A 95% confidence interval does NOT mean there is a 95% probability that the true value lies within the interval. It means: if you repeated your experiment 100 times and computed a confidence interval each time, approximately 95 of those intervals would contain the true population parameter. The true value is fixed — it is either in your interval or it is not. The probability applies to the procedure, not to any single interval.

#Statistics#Definitions#CommonMisconceptions
Tool SpotlightMar 31

DuckDB: Run SQL on local files without a database server

DuckDB is an in-process analytical database that runs entirely in memory or on local files — no server, no setup, no credentials. You can query CSV, Parquet, and JSON files directly with standard SQL: SELECT * FROM 'data.parquet' WHERE year = 2025. It is orders of magnitude faster than pandas for analytical queries on datasets up to a few GB. Install with pip install duckdb. For data exploration and local ETL, it is one of the most useful tools added to the data stack in recent years.

#Tools#SQL#DataEngineering#DuckDB
Pro TipMar 30

Use EXPLAIN ANALYZE before optimizing any slow query

Before adding an index or rewriting a query, always run EXPLAIN ANALYZE (PostgreSQL) or EXPLAIN FORMAT=JSON (MySQL) to see the actual execution plan. The most common culprits for slow queries are sequential scans on large tables, nested loop joins on unindexed columns, and sort operations that spill to disk. The query planner's cost estimates tell you where to focus — do not optimize blindly.

#SQL#Performance#Database
Chart InsightMar 29

Why your bar chart's y-axis should almost always start at zero

Truncating the y-axis of a bar chart — starting it at a value other than zero — visually exaggerates differences between bars. A bar representing 102 will look twice as tall as one representing 101 if the axis starts at 100. This is not just a stylistic choice: it is a form of data distortion. The exception is when you are showing change over time on a line chart, where starting at zero can compress meaningful variation. For bar charts comparing absolute values, zero is the rule.

#DataVisualization#Statistics#BestPractices
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